Hurricane Ian wreaked havoc over Florida in late September, leaving a trail of destruction. Some people in three of the worst hit counties saw an unexpected beacon of hope a week after the storm.
Thousands of people in the three counties received a push notification on their phones offering $700 in cash assistance. Satellite images showed that people living in badly damaged neighborhoods needed some help.
In the months after the world's economy was crippled by the Pandemic, GiveDirectly tested a similar idea in Africa. Clues from cell phone bills and signs of poverty were used to help households.
The Florida project was powered by a mapping tool called Delphi, developed by four machine-learning experts who worked with GiveDirectly over six months. The software highlights communities in need after disasters by showing live maps of storm damage with data on poverty from sources such as the US Centers for Disease Control and Prevention. The storm damage data is provided by another tool that uses machine learning to analyze satellite imagery from before and after a disaster and estimate the severity of damage to buildings.
There is a map that shows where socio-economically vulnerable and where has been damaged. It can speed up delivery of aid.